Advances in deep neural network (DNN) based molecular property prediction
have recently led to the development of models of remarkable accuracy and
generalization ability, with graph convolution neural networks (GCNNs)
reporting state-of-the-art performance for this task. However, some challenges
remain and one of the most important that needs to be fully addressed concerns
uncertainty quantification. DNN performance is affected by the volume and the
quality of the training samples. Therefore, establishing when and to what
extent a prediction can be considered reliable is just as important as
outputting accurate predictions, especially when out-of-domain molecules are
targeted. Recently, several methods to account for uncertainty in DNNs have
been proposed, most of which are based on approximate Bayesian inference. Among
these, only a few scale to the large datasets required in applications.
Evaluating and comparing these methods has recently attracted great interest,
but results are generally fragmented and absent for molecular property
prediction. In this paper, we aim to quantitatively compare scalable techniques
for uncertainty estimation in GCNNs. We introduce a set of quantitative
criteria to capture different uncertainty aspects, and then use these criteria
to compare MC-Dropout, deep ensembles, and bootstrapping, both theoretically in
a unified framework that separates aleatoric/epistemic uncertainty and
experimentally on the QM9 dataset. Our experiments quantify the performance of
the different uncertainty estimation methods and their impact on
uncertainty-related error reduction. Our findings indicate that ensembling and
bootstrapping consistently outperform MC-Dropout, with different
context-specific pros and cons. Our analysis also leads to a better
understanding of the role of aleatoric/epistemic uncertainty and highlights the
challenge posed by out-of-domain uncertainty.

本研究旨在定量比较 GCNNs 中不确定性估计的可扩展技术，介绍了一组定量标准来捕捉不同方面的不确定性，并使用这些标准在 QM9 数据集上理论和实验比较了 MC-Dropout、deep ensembles 和 bootstrapping 这些不确定估计方法，结果表明合奏和自举法始终优于 MC-Dropout 方法，在不同的具体情境中具有不同的优点和缺点，并突出了来自不同领域的不确定性所带来的挑战。

基于 DNN 的分子性质预测的可扩展不确定性估计方法评估

Evaluating Scalable Uncertainty Estimation Methods for DNN-Based  Molecular Property Prediction

Network biology has been successfully used to help reveal complex mechanisms
of disease, especially cancer. On the other hand, network biology requires
in-depth knowledge to construct disease-specific networks, but our current
knowledge is very limited even with the recent advances in human cancer
biology. Deep learning has shown a great potential to address the difficult
situation like this. However, deep learning technologies conventionally use
grid-like structured data, thus application of deep learning technologies to
the classification of human disease subtypes is yet to be explored. Recently,
graph based deep learning techniques have emerged, which becomes an opportunity
to leverage analyses in network biology. In this paper, we proposed a hybrid
model, which integrates two key components 1) graph convolution neural network
(graph CNN) and 2) relation network (RN). We utilize graph CNN as a component
to learn expression patterns of cooperative gene community, and RN as a
component to learn associations between learned patterns. The proposed model is
applied to the PAM50 breast cancer subtype classification task, the standard
breast cancer subtype classification of clinical utility. In experiments of
both subtype classification and patient survival analysis, our proposed method
achieved significantly better performances than existing methods. We believe
that this work is an important starting point to realize the upcoming
personalized medicine.

本文提出了一种混合模型，该模型将图卷积神经网络和关系网络组合在一起，在乳腺癌亚型分类和患者存活分析的实验中，该模型表现出了比现有方法更好的性能。